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In machine learning, predictors trained on a given data distribution are usually guaranteed to perform well for further examples from the same distribution on average. This often may involve disregarding or diminishing the predictive power on atypical examples; or, in more extreme cases, a data distribution may be composed of a mixture of individually "atypical" heterogeneous populations, and the kind of simple predictors we can train may find it difficult to fit all of these populationsdoi:10.1609/aaai.v34i04.5864 fatcat:ifcuh7urjvgjrai3hi4kgpxcrq